{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline for the anomaly detection on the SKAB using Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries importing\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import IsolationForest\n",
    "import shap\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# additional modules\n",
    "import sys\n",
    "sys.path.append('../utils')\n",
    "from evaluating import evaluating_change_point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# benchmark files checking\n",
    "all_files=[]\n",
    "import os\n",
    "for root, dirs, files in os.walk(\"../data/\"):\n",
    "    for file in files:\n",
    "        if file.endswith(\".csv\"):\n",
    "             all_files.append(os.path.join(root, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasets with anomalies loading\n",
    "list_of_df = [pd.read_csv(file, \n",
    "                          sep=';', \n",
    "                          index_col='datetime', \n",
    "                          parse_dates=True) for file in all_files if 'anomaly-free' not in file]\n",
    "# anomaly-free df loading\n",
    "anomaly_free_df = pd.read_csv([file for file in all_files if 'anomaly-free' in file][0], \n",
    "                            sep=';', \n",
    "                            index_col='datetime', \n",
    "                            parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data description and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A number of datasets in the SkAB v1.0: 34\n",
      "\n",
      "Shape of the random dataset: (1154, 10)\n",
      "\n",
      "A number of changepoints in the SkAB v1.0: 130\n",
      "\n",
      "A number of outliers in the SkAB v1.0: 13241\n",
      "\n",
      "Head of the random dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accelerometer1RMS</th>\n",
       "      <th>Accelerometer2RMS</th>\n",
       "      <th>Current</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Thermocouple</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Volume Flow RateRMS</th>\n",
       "      <th>anomaly</th>\n",
       "      <th>changepoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:36</th>\n",
       "      <td>0.027429</td>\n",
       "      <td>0.040353</td>\n",
       "      <td>0.770310</td>\n",
       "      <td>0.382638</td>\n",
       "      <td>71.2129</td>\n",
       "      <td>25.0827</td>\n",
       "      <td>219.789</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:37</th>\n",
       "      <td>0.027269</td>\n",
       "      <td>0.040226</td>\n",
       "      <td>1.096960</td>\n",
       "      <td>0.710565</td>\n",
       "      <td>71.4284</td>\n",
       "      <td>25.0863</td>\n",
       "      <td>233.117</td>\n",
       "      <td>32.0104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:38</th>\n",
       "      <td>0.027040</td>\n",
       "      <td>0.039773</td>\n",
       "      <td>1.140150</td>\n",
       "      <td>0.054711</td>\n",
       "      <td>71.3468</td>\n",
       "      <td>25.0874</td>\n",
       "      <td>234.745</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:39</th>\n",
       "      <td>0.027563</td>\n",
       "      <td>0.040313</td>\n",
       "      <td>1.108680</td>\n",
       "      <td>-0.273216</td>\n",
       "      <td>71.3258</td>\n",
       "      <td>25.0897</td>\n",
       "      <td>205.254</td>\n",
       "      <td>32.0104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:41</th>\n",
       "      <td>0.026570</td>\n",
       "      <td>0.039566</td>\n",
       "      <td>0.704404</td>\n",
       "      <td>0.382638</td>\n",
       "      <td>71.2725</td>\n",
       "      <td>25.0831</td>\n",
       "      <td>212.095</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accelerometer1RMS  Accelerometer2RMS   Current  Pressure  \\\n",
       "datetime                                                                        \n",
       "2020-03-09 12:14:36           0.027429           0.040353  0.770310  0.382638   \n",
       "2020-03-09 12:14:37           0.027269           0.040226  1.096960  0.710565   \n",
       "2020-03-09 12:14:38           0.027040           0.039773  1.140150  0.054711   \n",
       "2020-03-09 12:14:39           0.027563           0.040313  1.108680 -0.273216   \n",
       "2020-03-09 12:14:41           0.026570           0.039566  0.704404  0.382638   \n",
       "\n",
       "                     Temperature  Thermocouple  Voltage  Volume Flow RateRMS  \\\n",
       "datetime                                                                       \n",
       "2020-03-09 12:14:36      71.2129       25.0827  219.789              32.0000   \n",
       "2020-03-09 12:14:37      71.4284       25.0863  233.117              32.0104   \n",
       "2020-03-09 12:14:38      71.3468       25.0874  234.745              32.0000   \n",
       "2020-03-09 12:14:39      71.3258       25.0897  205.254              32.0104   \n",
       "2020-03-09 12:14:41      71.2725       25.0831  212.095              33.0000   \n",
       "\n",
       "                     anomaly  changepoint  \n",
       "datetime                                   \n",
       "2020-03-09 12:14:36      0.0          0.0  \n",
       "2020-03-09 12:14:37      0.0          0.0  \n",
       "2020-03-09 12:14:38      0.0          0.0  \n",
       "2020-03-09 12:14:39      0.0          0.0  \n",
       "2020-03-09 12:14:41      0.0          0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dataset characteristics printing\n",
    "print(f'A number of datasets in the SkAB v1.0: {len(list_of_df)}\\n')\n",
    "print(f'Shape of the random dataset: {list_of_df[0].shape}\\n')\n",
    "n_cp = sum([len(df[df.changepoint==1.]) for df in list_of_df])\n",
    "n_outlier = sum([len(df[df.anomaly==1.]) for df in list_of_df])\n",
    "print(f'A number of changepoints in the SkAB v1.0: {n_cp}\\n')\n",
    "print(f'A number of outliers in the SkAB v1.0: {n_outlier}\\n')\n",
    "print(f'Head of the random dataset:')\n",
    "display(list_of_df[0].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# random dataset visualizing\n",
    "list_of_df[0].plot(figsize=(12,6))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Signals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the labels both for outlier and changepoint detection problems\n",
    "list_of_df[0].anomaly.plot(figsize=(12,3))\n",
    "list_of_df[0].changepoint.plot()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method applying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# classifier initializing\n",
    "clf = IsolationForest(random_state=0, \n",
    "                      n_jobs=-1,\n",
    "                      contamination=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inference\n",
    "predicted_outlier, predicted_cp = [], []\n",
    "for df in list_of_df:\n",
    "    X_train = df[:400].drop(['anomaly','changepoint'], axis=1)\n",
    "    \n",
    "    # classifier fitting\n",
    "    clf.fit(X_train)\n",
    "\n",
    "    # results predicting\n",
    "    prediction = pd.Series(clf.predict(df.drop(['anomaly','changepoint'], axis=1))*-1, \n",
    "                                index=df.index).rolling(3).median().fillna(0).replace(-1,0)\n",
    "    \n",
    "    # predicted outliers saving\n",
    "    predicted_outlier.append(prediction)\n",
    "    \n",
    "    # predicted CPs saving\n",
    "    prediction_cp = abs(prediction.diff())\n",
    "    prediction_cp[0] = prediction[0]\n",
    "    predicted_cp.append(prediction_cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# true outlier indices selection\n",
    "true_outlier = [df.anomaly for df in list_of_df]\n",
    "\n",
    "predicted_outlier[0].plot(figsize=(12,3), label='predictions', marker='o', markersize=5)\n",
    "true_outlier[0].plot(marker='o', markersize=2)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# true changepoint indices selection\n",
    "true_cp = [df.changepoint for df in list_of_df]\n",
    "\n",
    "predicted_cp[0].plot(figsize=(12,3), label='predictions', marker='o', markersize=5)\n",
    "true_cp[0].plot(marker='o', markersize=2)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Alarm Rate 6.86 %\n",
      "Missing Alarm Rate 72.09 %\n",
      "F1 metric 0.4\n"
     ]
    }
   ],
   "source": [
    "# binary classification metrics calculation\n",
    "binary = evaluating_change_point(true_outlier, predicted_outlier, metric='binary', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average delay 0 days 00:00:08.821917808\n",
      "A number of missed CPs = 57\n"
     ]
    }
   ],
   "source": [
    "# average detection delay metric calculation\n",
    "add = evaluating_change_point(true_cp, predicted_cp, metric='average_delay', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Standart  -  37.53\n",
      "LowFP  -  17.09\n",
      "LowFN  -  45.02\n"
     ]
    }
   ],
   "source": [
    "# nab metric calculation\n",
    "nab = evaluating_change_point(true_cp, predicted_cp, metric='nab', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Additional] localization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shap repository with explonations [link](https://github.com/slundberg/shap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load JS visualization code to notebook\n",
    "# shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create shap values and plot them\n",
    "# X_explain = list_of_df[0][:'2019-07-08 18:30']#[pd.Series(prediction, index=data.index)==-1]\n",
    "\n",
    "# explainer = shap.TreeExplainer(clf)\n",
    "# shap_values = explainer.shap_values(X_explain)\n",
    "\n",
    "# shap.summary_plot(shap_values, X_explain, plot_type='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create shap values and plot them\n",
    "# X_explain = data['2019-07-08 18:30':'2019-07-08 18:50']#[pd.Series(prediction, index=data.index)==-1]\n",
    "\n",
    "# explainer = shap.TreeExplainer(clf)\n",
    "# shap_values = explainer.shap_values(X_explain)\n",
    "\n",
    "# shap.summary_plot(shap_values, X_explain, plot_type='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shap.force_plot(explainer.expected_value, shap_values, X_explain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
